**Amirhossein Sadoghi** (Frankfurt School of Finance & Management) Measuring Systemic Risk: Robust Ranking Techniques Approach
The recent economic crisis has raised a wide awareness that the financial system should be viewed as a complex network with financial institutions and financial dependencies respectively as nodes and links between these nodes.
Systemic risk is defined as the risk of default of a large portion of financial exposures among institution in the network. Indeed, the structure of this network is an important element to measure systemic risk and to determine the systemically important nodes in a large financial network. Typically in the real-world financial network, there are some disconnected subgraphs as well as cycles and current solutions for cyclic matrix it may converge slowly and not optimum.
In this research, we introduce a metric for systemic risk measurement with taking into account both common idiosyncratic shocks as well as contagion through counterparty exposures.
Our focus is on application of Eigenvalue problems, as a robust approach to the ranking techniques, to measure systemic risk.
We show how the Eigenvalue problem reduces to a non-smooth convex optimization problem and it can be solved in the efficient way. We applied this technique and studied the performance and convergence behavior of the algorithm with different structure of the financial network. Moreover, we analyzed some financial regulations to mitigate the probability of failure based on both node and link structure of the network.
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